2 citations · 2 across the 11 of their papers we have counts for
12 papers
An Interactive Agent for Requirement-Driven Candidate Sourcing
Yuanpeng He, Fangjing Li, Xiangyu Ru +10
Finding people from a natural-language description (``ML engineers transitioning to research roles in biotech'') is increasingly delegated to LLM agents and framed as information r…
PRAXIS: Graph-Grounded Tacit Knowledge for Domain Code Generation
Xue Jiang, Tianyu Zhang, Lingwei Wu +7
LLM agents have achieved strong performance on general software engineering tasks, yet struggle with domain-specific code generation. We identify the root cause as the agent's lack…
Think Anywhere in Code Generation
Xue Jiang, Tianyu Zhang, Ge Li +8
Recent advances in reasoning Large Language Models (LLMs) have primarily relied on upfront thinking, where reasoning occurs before final answer. However, this approach suffers from…
Synthesizing File-Level Data for Unit Test Generation with Chain-of-Thoughts via Self-Debugging
Ziyue Hua, Tianyu Chen, Yeyun Gong +8
Automatic unit test (UT) generation is essential for software quality assurance, but existing approaches--including symbolic execution, search-based approaches, and recent LLM-base…
KOCO-BENCH: Can Large Language Models Leverage Domain Knowledge in Software Development?
Xue Jiang, Ge Li, Jiaru Qian +12
Large language models (LLMs) excel at general programming but struggle with domain-specific software development, necessitating domain specialization methods for LLMs to learn and…
CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment
Xue Jiang, Yihong Dong, Mengyang Liu +10
While Large Language Models (LLMs) excel at code generation by learning from vast code corpora, a fundamental semantic gap remains between their training on textual patterns and th…
Large Language Model Unlearning for Source Code
Xue Jiang, Yihong Dong, Huangzhao Zhang +9
While Large Language Models (LLMs) excel at code generation, their inherent tendency toward verbatim memorization of training data introduces critical risks like copyright infringe…
Enhancing LLM Generation with Knowledge Hypergraph for Evidence-Based Medicine
Chengfeng Dou, Ying Zhang, Zhi Jin +4
Evidence-based medicine (EBM) plays a crucial role in the application of large language models (LLMs) in healthcare, as it provides reliable support for medical decision-making pro…
ROCODE: Integrating Backtracking Mechanism and Program Analysis in Large Language Models for Code Generation
Xue Jiang, Yihong Dong, Yongding Tao +4
Large language models (LLMs) have achieved impressive performance in code generation recently, offering programmers revolutionary assistance in software development. However, due t…
Exploring LLM-based Data Annotation Strategies for Medical Dialogue Preference Alignment
Chengfeng Dou, Ying Zhang, Zhi Jin +4
This research examines the use of Reinforcement Learning from AI Feedback (RLAIF) techniques to improve healthcare dialogue models, with the aim of tackling the challenges of prefe…
Integrating Physician Diagnostic Logic into Large Language Models: Preference Learning from Process Feedback
Chengfeng Dou, Zhi Jin, Wenpin Jiao +3
The use of large language models in medical dialogue generation has garnered significant attention, with a focus on improving response quality and fluency. While previous studies h…
PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning
Chengfeng Dou, Zhi Jin, Wenping Jiao +3
The patient-centered medical dialogue systems strive to offer diagnostic interpretation services to users who are less knowledgeable about medical knowledge, through emphasizing th…